58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
"""
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Testing Recursive feature elimination
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"""
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from nose.tools import assert_true
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from sklearn.feature_selection.rfe import RFE, RFECV
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from sklearn.datasets import load_iris
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from sklearn.metrics import zero_one
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from sklearn.svm import SVC
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from sklearn.utils import check_random_state
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def test_rfe():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = iris.target
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clf = SVC(kernel="linear")
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rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
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rfe.fit(X, y)
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X_r = rfe.transform(X)
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assert_true(X_r.shape == iris.data.shape)
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assert_array_almost_equal(X_r[:10], iris.data[:10])
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assert_array_almost_equal(rfe.predict(X), clf.predict(iris.data))
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assert_true(rfe.score(X, y) == clf.score(iris.data, iris.target))
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def test_rfecv():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = iris.target
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# Test using the score function
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rfecv = RFECV(estimator=SVC(kernel="linear", C=100), step=1, cv=3)
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rfecv.fit(X, y)
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X_r = rfecv.transform(X)
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assert_true(X_r.shape == iris.data.shape)
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assert_array_almost_equal(X_r[:10], iris.data[:10])
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# Test using a customized loss function
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rfecv = RFECV(estimator=SVC(kernel="linear", C=100), step=1, cv=3,
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loss_func=zero_one)
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rfecv.fit(X, y)
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X_r = rfecv.transform(X)
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assert_true(X_r.shape == iris.data.shape)
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assert_array_almost_equal(X_r[:10], iris.data[:10])
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